Finance Data Workshop [ECN339]

A Guide to Setting-up R and RStudio

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What is R?

• R is an opensource powerful programming language suited to mathematical and statistical computing, analysis and graphics.

  • Download R from here: https://www.r-project.org/

• RStudio provides a convenient interface for using R. Both R and RStudio are free.

  • Download RStudio (free Desktop Open Source Edition) from here: https://www.rstudio.com/
    
  • RStudio Cloud provides R and RStudio via a web browser. You can make a free account but the amount of time you can use the service for free is limited. This may be a backup option if you have difficulties setting up the software on your own computer, but you will not get enough time to complete the whole course under a free account. Remember to copy any files you make from RStudio Cloud to your own computer so that you do not risk losing your work. RStudio Cloud can be found here: https://rstudio.cloud/

• We will use R via RStudio in this course to analyse finance data and estimate finance models.

• As we go through the analyses and models in class, please enter the code and execute it on your computer.

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After installing the software, open RStudio

• It should look something like this.

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Set the folder RStudio uses when not in a project

• From “Preferences” set the default folder to your documents or other convenient folder.

• I set mine to my Dropbox folder.

• After you have set the folder, quit and reopen RStudio.

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Make a “project” for this course

• A project is a folder where you can keep data, code and documents together.

• You can have several different projects and set R to start up differently for each project. Projects allow you to keep your files organised, and let you customise how R works.

• If you use R for other classes, your seminar or other purposes, you can create other projects for those.

• Each project will have a separate folder and files to save the data in your environment, your history of code executed in the console, and the way you have R configured.

• Make a new project from the Projects menu at the top-right of the screen.

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Click “New Directory” for your project

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Select “New Project”

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Give the project folder a name

• I use the name “portfoliomngt.”

  • I originally made this guide for my course Portfolio Managment. For Finance Data Workshop, you can use a more approporiate name, such as “FDW.”

• Click “Create Project.”

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Your desktop should look something like this

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Set the “Project Options”

• Find the “Project Options” from the project menu at the top-right of your screen.

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Set the three options to “Yes”

• This means your data and history of commands will be saved quen you quit and restored when you open your project the next time.

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RStudio has four panes

• Pane 1 is the source pane where you can open and work on various types of code files or documents. You also display objects in the source pane.

  • R is an object oriented language which means everything is an object with certain attributes, e.g. a matrix of data or a function that transforms data.

  • Objects can interact with each other to change their attributes, or create other objects.

  • Some important types of objects are: vectors, lists, arrays, matrices, tables, data frames, functions.

• Pane 2 is the console where you can enter and execute code.

• Pane 3 contains the environment and history tabs. The environment shows all objects that are loaded for use (it is empty now). The history tab shows the code you have executed in the console.

• Pane 4 contains a number of tabs showing your folders and files, plots you have drawn, packages, a help window.

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Packages

• Packages extend the functionality of R.

• Packages contain functions, data and other objects. They are designed to perform certain types of data manipulation, analysis, estimation, visualisation, optimisation, document creation, etc.

• Some packages are part of the default R installation, called “Base R,” and there are a huge number of additional packages available that allow you to do almost anything with R.

• We will use several packages in this course.

• There are two steps to using a package.

  1. Install the package. This downloads the latest verson of the package from an R repository and saves it on your computer.

  2. Load a package for use. When you want to use a package you must load it first with the library command.

• To install a package, click the install button, type in the name of the package and hit install.

• Install the following packages: AER, dynlm, fGarch, FinCal, kableExtra, MASS, reshape2, stargazer, tidyquant, tidyverse, wesanderson, xtable.

  • Note that R is case-sensitive, thus upper- and lower-case letters are interpreted as being different.

• Alternatively, install the packages by running the following code in the console.

install.packages("AER", "dynlm", "fGarch", "FinCal", "kableExtra", "MASS", "reshape2", "stargazer", "tidyquant", "tidyverse", "wesanderson", "xtable", dependencies = TRUE)

• Installation of packages will take some time. Our list of packages will require several additional packages, callede dependencies, to be downloaded.

  • If you are asked “Do you want to install from sources the packages which need compilation? (Yes/no/cancel)” during package installation, you can answer “n.”

  • If an installation results in a message like “Installation of package X had non-zero exit status” the package was not installed. Try again. If you still have a problem, contact me.

  • A message like “The downloaded source packages are in ‘folder location’” means the packages were successfully installed.

• Packages need only be installed on your computer once, but they should be updated from time to time.

• When you wish to use a package, it must be loaded using the library command. For example, to load the AER package:

library(AER)

• Another way to load a package is to tick the box next to the package in the Packages tab. The packages tab shows you all packages that you have installed.

• If you get an error saying R cannot find a function when you are trying to execute code, it is almost always because you have either a) not loaded the package that contains the function, or b) mistyped the function.

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Where are my files?

• The working directory is where R will look for your files and save files.

• Show your current working directory:

getwd()

• The working directory is set by your project.

• If you want to change your working directory temporarily you can set it to another path. Here is an example for my folders:

setwd("/Users/Clinton/Dropbox/syncd_r_data/econometrics")

• If you your working directory is a folder on a network drive or on the cloud (like Dropbox or Google Drive) that is synced to your computer’s hard drive, you will be able to use it with RStudio on multiple computers.

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Special HIDDEN R files in your working directory

• RStudio uses three important hidden files. You can see them in the RStudio files pane but usually not in your operating system.

  • .RData holds the objects that are loaded in your environment.

  • .RHistory holds the history of the code you have executed in the console.

  • .Rprofile holds specific set-up information for the project you are working in.

• Here are the hidden files in my working directory shown in red and the project file which holds information about my project in blue.:

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Using the “.Rprofile” file to configure an R session

• The .Rprofile file executes code as R starts up. You can use this file to automatically load the packages you will need to use for this course and to set some R options. Thus .Rprofile allows you to customise the start-up of R.

  • Remember that the packages must already be installed on your computer to be loaded by the .Rprofile file.

• Here is the code you will need in your .Rprofile file for this course.

# Portfolio Management .Rprofile file

# Auto-load packages:
library(AER)
library(broom)
library(data.table)
library(dynlm)
library(fBasics)
library(fGarch)
library(FinCal)
library(ggplot2)
library(kableExtra)
library(magrittr)
library(MASS)
library(matrixStats)
library(quadprog)
library(quantmod)
library(RColorBrewer)
library(reshape2)
library(rmarkdown)
library(stargazer)
library(timeSeries)
library(tseries)
library(wesanderson)
library(xtable)
library(xts)
library(tidyquant)
library(tidyverse)

# Set some of R options:
options(prompt="R> ", scipen=999, digits=4, width=80)

• To make your .Rprofile file, follow these steps.

• Create a new R Script file.

• Your R Script file is shown below. An R Script file is a text file that you can save code in.

• Copy all of the code from the Portfolio Management .Rprofile file above and paste it into your R Script file.

• Save the R Script file in your working directory (your project directory). The file name does not matter for now.

• Rename the file as “.Rprofile.”

• You should now have your .Rprofile file in your working directory. If you restart RStudio, your packages should load automatically.

• You may edit the .Rprofile file to add library commands for additional packages you might want to load when R starts up.

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Rmarkdown and Notebooks

• Rmarkdown allows dynamic documents to be created in R that contain written text, R code and output from the code.

• I will ask you to do some assignments in an R Notebook using markdown.

  • This guide for setting-up R and RStudio was made in an R Notebook. If you look at the top of the page you can see a button labelled “Code” which alows you to download the code I used to make this HTML file.

• A markdown document is written in using coding called markdown, which is an easy-to-write plain text format that is related to TeX (or LaTeX).

• A TeX installation is required to use Rmarkdown, such as MacTex (for Mac OS) http://www.tug.org/mactex/ or MiKTeX (cross-platform) https://miktex.org/download. If you use LaTeX to write documents, you will already have this. If not, the easiest way to get a TeX installation for markdown is via the TinyTeX package.

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Install TinyTeX using the package called tinytex

• You have already installed the package called tinytex. Executing the code below in the console will download the TinyTeX files from the internet and install them on your computer so that you can make markdown documents.

tinytex::install_tinytex()

• This will take some time to download.

• Note that you do not need to do this if you use RStudio Cloud as TinyTeX is pre-installed.

• When the installation has finished, restart RStudio.

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Test your Rmarkdown environment

• Run through a simple test to see if you can make an R Notebook, as follows.

• Open a new R Notebook.

• The notebook already contains example text and R code that plots a simple chart.

• Run the code by pressing “Run All” from the “Run” menu.

• After you have run the code, you should see the plot appear.

• Include your name as I have at the top of the Notebook.

• Save the file to your working directory.

• You should see two files appear.

  • Your code is in the file with the extension “.Rmd.”

  • Your HTML R Notebook has the extension “.nb.html.”

• Open the HTML file with your browser. It should look like this:

• Well done! You have created a Notebook.

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Information on Rmarkdown and TinyTeX

• Information on Rmarkdown can be found at https://rmarkdown.rstudio.com/ and a useful guide is at https://bookdown.org/yihui/rmarkdown/ and https://bookdown.org/yihui/rmarkdown-cookbook/ and https://bookdown.org/yihui/rmarkdown/. Easy and helpful video lessons can be found at https://rmarkdown.rstudio.com/lesson-1.html.

• Reference material for TinyTeX is at https://yihui.name/tinytex/.

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Resources for R

• There are many guides to using R available online, both documents and videos.

• A comprehensive introduction is available on AIMS Venables, Smith, and R Core Team (2019) and at https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.

• Other R documentation and guides for various applications contributed by R users can be found here: https://cran.r-project.org/other-docs.html#english.

• My RA & TA at Kobe University produced the following guides, available at Massimiliano’s R Page on GitHub.

  • R for Beginners
    
  • R Markdown: Notes for Editing
    
  • LaTeX: Editing for Mathematics

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CRAN Task Views

• CRAN Task Views provide information about packages that can be used with R. Packages extend the functionality of R. They provide routines for various types of data manipulation, mathematical and econometric models, optimisation methods, financial models and more. Packages are constantly being developed and updated by R users.

• The most useful Task View for this course is:

  • Empirical finance: https://cran.r-project.org/web/views/Finance.html

• These Task Views may also be helpful:

  • Econometrics: https://cran.r-project.org/web/views/Econometrics.html

  • Time series: https://cran.r-project.org/web/views/TimeSeries.html

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Books on R

• Many books have been published on using R, programming with R, and using R for finance.

  • Finance: Ang (2021), Bennett and Hugen (2016), Andrecut (2010), Tsay (2010), Tsay (2014).

  • Econometrics: Kleiber and Zeileis (2008), Cowpertwait and Metcalfe (2009), Pfaff (2008).

  • Coding: Matloff (2011).

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R-Bloggers

• R-Bloggers is a blog site containing many helpful posts on using R: http://www.r-bloggers.com/

• The posts on the site show the wide variety of problems that coding in R can be applied to (not just finance). For example:

  • https://www.r-bloggers.com/who-wrote-that-anonymous-nyt-op-ed-text-similarity-analyses-with-r/

  • https://www.r-bloggers.com/how-to-build-animated-bar-plots-using-r/

• A good introduction to learning R: https://www.r-bloggers.com/how-to-learn-r-2/

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Search

• Lots of information is posted online by R users including questions and answers.

• If you are stuck, search for information that will help you answer your question.

• rseek is a search engine for R: www.rseek.org

• Using another search engine like Google:

  • “filetype:R regression –rebol”

  • “CRAN regression”

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RStudio Keyboard Shortcuts (for Mac)

• Code completion:
  • Type part of the function you want and then press the Tab key.
    
  • Also works for function arguments, that is press Tab when yo uare inside the brackets of a function.
    
  • Also recalls object names.

• Press F1 (or fn+F1) for help on a function.

• Retrieve previous commands:

  • Press the up or down arrows to scroll through your code history.

• View a list of previous commands:
  • Press Control (or Command) and the up arrow.

• View a list of previous commands that match a prefix:
  • Type the prefix and press Control (orCommand) and the up arrow.

• Enter a line of code from an R script to the console:
  • Command and enter.

• Enter a line of code from history to and R script:
  • Shift and enter.

• More:
  • Tools -> Keyboard Shortcuts Help
    
  • https://support.rstudio.com/hc/en-us/articles/200711853

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© Copyright Clinton Watkins 2021

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References

Andrecut, M. 2010. “Portfolio Optimization in R.” Cornell University, arXiv:1307.0450 [q-fin.PM]. https://doi.org/10.1201/b17178.

Ang, Clifford S. 2021. Analyzing Financial Data and Implementing Financial Models Using R. Second. Springer Texts in Business and Economics. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-64155-9.

Bennett, Mark J., and Dirk L. Hugen. 2016. Financial Analytics with R: Building a Laptop Laboratory for Data Science. Cambridge University Press. https://doi.org/https://doi.org/10.1017/CBO9781316584460.

Cowpertwait, Paul S. P., and Andrew V. Metcalfe. 2009. Introductory Time Series with R. Springer. https://doi.org/10.1007/978-0-387-88698-5.

Kleiber, Christian, and Achim Zeileis. 2008. Applied Econometrics with R. 1st ed. New York: Springer US. https://doi.org/10.1007/978-0-387-77318-6.

Matloff, Norman. 2011. The Art of R Programming: A Tour of Statistical Software Design. No Starch Press. https://www.nostarch.com/artofr.htm.

Pfaff, Bernhard. 2008. Analysis of Integrated and Cointegrated Time Series with R. Second. New York: Springer-Verlag. papers2://publication/uuid/2D6BDE05-D712-4D3C-99CD-72EADA215C83 http://www.springer.com/gp/book/9780387759661.

Tsay, Ruey S. 2010. Analysis of financial time series. Wiley.

———. 2014. Multivariate time series analysis : with R and financial applications. Wiley.

Venables, W. N., D. M. Smith, and R Core Team. 2019. “An Introduction to R.” https://doi.org/10.1201/9781420035025.ch1.